Last week, I was doing some testing using the Python API interface for sentinel-1 toolbox with Snappy and the SNAP desktop to see the time taken by each of them. The workflow for processing the data is described here. The results were astonishing. Each and every steps were significantly slower, especially the co-registration process that took 8-10 mins in the SNAP desktop took around 2.75 hours, which was very disappointing. There are few discussions on the SNAP STEP Forum, but most of them, at the end, started using the Graph Processor Tool (GPT) to process the data. GPT is based in JAVA, and has parallel processing that makes it utilize the available resources of the machine to speed up the process. The SNAP Desktop on the background uses the GPT as well.

Turns out, switching from using Snappy to GPT is fairly simple. Usually, a graph looks like below. This is an example graph representing Ellipsoid Correction through Geo-location Grid.